Design of voltage-scalable meta-functions for approximate computing

Approximate computing techniques that exploit the inherent resilience in algorithms through mechanisms such as voltage over-scaling (VOS) have gained significant interest. In this work, we focus on meta-functions that represent computational kernels commonly found in application domains that demonstrate significant inherent resilience, namely Multimedia, Recognition and Data Mining. We propose design techniques (dynamic segmentation with multi-cycle error compensation, and delay budgeting for chained data path components) which enable the hardware implementations of these meta-functions to scale more gracefully under voltage over-scaling. The net effect of these design techniques is improved accuracy (fewer and smaller errors) under a wide range of over-scaled voltages. Results based on extensive transistor-level simulations demonstrate that the optimized meta-function implementations consume up to 30% less energy at iso-error rates, while achieving upto 27% lower error rates at iso-energy when compared to their baseline counterparts. System-level simulations for three applications, motion estimation, support vector machine based classification and k-means based clustering are also presented to demonstrate the impact of the improved meta-functions at the application level.

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